AI-Based Drug Abuse Detection: A Survey of Artificial Intelligence Approaches for Early Identification and Intervention

AI-Based Drug Abuse Detection: A Survey of Artificial Intelligence Approaches for Early Identification and Intervention

Shamna K, Fathima TP, Hida Sherin M, Dhanya V

Department of CSE College of Engineering, Vadakara

Abstract – Drug abuse among adolescents and young adults has emerged as a serious public health and social concern, leading to adverse effects on physical health, academic performance, and emotional well-being. Early detection of substance abuse remains challenging due to social stigma, lack of awareness, and the absence of scalable screening mechanisms. Conventional clinical detection methods are invasive, expensive, and unsuitable for continuous monitoring. Recent advances in Artificial Intelligence (AI) have enabled automated, non-invasive, and privacy-aware approaches for early drug abuse detection. This survey presents a structured review of existing AI-based drug abuse detection techniques, including behavioral and linguistic analysis, decision- based severity modeling, and neural network-based content detection. The paper highlights the strengths and limitations of current approaches and emphasizes the need for integrated and ethically deployable AI-assisted screening frameworks.

Index Terms – Drug Abuse Detection, Artificial Intelligence, Behavioral Analysis, Addiction Severity, CRAFFT, ASSIST

severity or behavioral patterns. These limitations highlight the need for non-invasive, scalable, and continuous screening mechanisms.

Recent advancements in Artificial Intelligence (AI) have enabled automated approaches for early drug abuse detection by analyzing behavioral patterns, linguistic cues, and decision- making indicators. AI-based systems offer privacy-preserving, scalable, and cost-effective solutions that can operate without biological testing. In addition, standardized screening tools such as CRAFFT and ASSIST are widely used to evaluate risky behavior and addiction severity. This review surveys existing AI-based drug abuse detection approaches and exam- ines their role in supporting early identification, monitoring, and ethical intervention in educational and healthcare environ- ments.

1. INTRODUCTION

   Drug abuse among adolescents and young adults is a growing social and public health concern that has serious consequences on physical health, academic performance, emo- tional stability, and social life. In many cases, substance use begins as a means of pleasure, curiosity, or stress relief and gradually progresses into addiction. Academic pressure, peer influence, emotional distress, family-related issues, and easy access to drugs significantly contribute to early experimenta- tion among students. Unfortunately, parents and teachers often fail to recognize early warning signs, which results in delayed intervention and severe addiction at later stages.

   Early detection plays a crucial role in preventing long-term health complications, academic decline, and social isolation. However, identifying substance abuse at an early stage is challenging due to social stigma, fear of judgment, and lack of accessible screening tools. Students are often hesitant to disclose their behavior voluntarily, which further complicates detection efforts. As a result, many cases remain undetected until addiction reaches an advanced stage.

   Traditional drug abuse detection methods primarily rely on laboratory-based tests such as blood, urine, saliva, hair, and nail analysis. Although these methods provide accurate results, they are invasive, costly, time-consuming, and impractical for routine or large-scale screening. Moreover, they focus on detecting the presence of drugs rather than assessing addiction

2. RELATED WORK

   This section presents a detailed review of existing research on AI-based drug abuse detection. Each study is discussed individually using the paper title as the subsection heading, following a survey-oriented analytical approach.

   1. Behavioral and Linguistic StyleBased Drug Abuse Detec-tion

      Behavioral and linguistic analysis has been widely explored for early detection of drug abuse through the examination of user-generated textual content and interaction patterns. Tseng and Li proposed a framework that combines user behavior with linguistic style analysis to identify early indicators of substance abuse [1]. Utilizing data from the WebMD medical community platform, their model applies Term Frequency- Inverse Document Frequency (TF-IDF) techniques to extract document characteristics and the VADER method to per- form sentiment analysis, categorizing user posts as positive, negative, or neutral [1]. Furthermore, their study calculated behavioral variance based on drug usability, effectiveness, and satisfaction after medication, noting that drug-abusing users exhibit a larger disparity in their experiences [1]. After comparing multiple machine learning models, Logistic Regres- sion emerged as the most accurate, achieving an accuracy of

      84.68 percentage [1]. Social media-based linguistic analysis further extends this approach. Sarker et al. demonstrated that large-scale discussions from platforms such as Reddit can

      reveal emotional expressions, substance use behaviors, and emerging drug trends using text mining and machine learning techniques [8]. These methods are effective for population- level monitoring and early trend detection [8]. Despite their strengths, behavioral and linguistic approaches rely heavily on textual data availability and raise privacy concerns. Moreover, they lack mechanisms for individual-level addiction severity assessment and integration with standardized screening tools

   2. Decision-Based and Severity Modeling Approaches

      Decision-based frameworks model addiction as a progres- sion of states rather than a single event. Kaur et al. intro- duced an artificial intelligence framework based on a Markov Decision Process (MDP) to classify individuals into low, moderate, and high addiction severity levels through repeated assessments [2]. Their system conducts a sequence of ques- tionnaires to calculate a probability (P ) that categorizes the users severity state [2]. If the probability exceeds 0.6, it indicates an alarming high-severity state requiring immediate treatment [2]. The algorithm dynamically computes the Total Per Unit Time (TPUT) for each test transition and tracks the Change Over Time (COT ) to measure whether the patients progress is improved, neutral, or declined [2]. This approach enables longitudinal monitoring of addiction behavior [2]. Standardized screening tools such as the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) and the Car, Relax, Alone, Forget, Family, Friends, Trouble (CRAFFT) questionnaire are widely used for severity assessment and risk evaluation [4], [5]. ASSIST provides a comprehensive assess- ment across multiple substances, whereas CRAFFT offers a brief screening mechanism particularly suitable for adolescents [4], [5]. Although effective, these tools rely on manual admin- istration and lack intelligent automation. Integrating AI-based decision models with standardized screening questionnaires can enhance scalability and continuous monitoring.

   3. Neural NetworkBased Content Identification

      Neural network-based approaches have been applied to identify drug-related communication in online platforms. Yang and Ding developed a method to identify network-related drug chat content by combining Radial Basis Function (RBF) neural networks with decision trees [3]. Their process in- volves establihing a drug linguistic feature attribute pool and utilizing a BMC discretization algorithm to optimize the processing of continuous attributes [3]. The RBF neural network screens for attributes with the highest classification accuracy to build the decision tree [3]. This hybrid approach successfully classified cyber drug crimes into four specific types: online drug trafficking, gathering people to take drugs, teaching drug crimes, and luring drug use [3]. The model demonstrated high accuracy, particularly in recognizing drug trafficking chats, boasting a comprehensive F value of 91 percentage [3]. However, these models focus solely on content classification and do not assess individual addiction severity or provide intervention mechanisms, limiting their use as standalone detection systems.

   4. Machine Learning Approaches for Substance Use Disorder Prediction

      Machine learning techniques have been extensively ex- plored for predicting substance use disorders using structured healthcare and behavioral datasets. Prieto et al. evaluated multiple supervised learning models and demonstrated their effectiveness in identifying individuals at risk of addiction [6]. Chekroud et al. extended this work by developing a machine learning framework capable of predicting opioid misuse across multiple clinical trials, highlighting the generalizability of such models [9]. Despite strong predictive performance, these ap- proaches rely on structured clinical data and do not incorporate early behavioral indicators relevant to students

   5. Social MediaBased Monitoring and Toxicovigilance

      Social media platforms have been explored as valuable data sources for monitoring substance abuse trends at the popula- tion level. Chary et al. investigated the use of social networks for toxicovigilance, enabling early detection of emerging drug misuse patterns through real-time monitoring [7]. McCabe et al. analyzed patterns of medical and nonmedical prescription opioid use among youth, emphasizing the prevalence of misuse and associated behavioral factors [10]. While effective for trend analysis, these approaches raise privacy concerns and do not support individual-level severity assessment.

   6. Brief Interventions and Early Prevention Strategies

   Early intervention plays a critical role in preventing sub- stance abuse progression. Humeniuk et al. reviewed brief inter- vention strategies and highlighted their effectiveness in reduc- ing substance use when applied at early stages [11]. Integrating AI-based screening systems with intervention frameworks can improve timely referral and personalized support.

3. PROBLEM DEFINITION AND MOTIVATION

   Despite significant advancements in AI-based drug abuse detection, existing systems exhibit several limitations. Tradi- tional laboratory-based methods are invasive, expensive, and unsuitable for early or continuous screening. Many AI-based approaches rely on isolated data sources such as question- naires, online text, or chat content, resulting in fragmented detection mechanisms that do not provide a holistic view of addiction behavior.

   Another major limitation is the lack of integration between AI models and standardized screening tools such as CRAFFT and ASSIST [4], [5]. These tools are widely accepted in clin- ical and educational settings but are often used independently without intelligent automation. As a result, addiction severity assessment and continuous monitoring remain inadequate. Furthermore, many AI-based systems operate as black-box models, raising concerns related to explainability, trust, and ethical deployment. This is particularly critical when screening adolescents and students, where incorrect classification may lead to stigmatization or psychological harm. There is also a lack of institution-centric systems that support parents, teachers, and counselors in early detection and intervention.

   The motivation for this review arises from the need to analyze existing AI-based drug abuse detection approaches, identify research gaps, and emphasize the importance of inte- grated screening frameworks. Combining behavioral analysis,

   decision-based modeling, neural network-based content detec- tion, and validated screening tools can enable early identifica- tion, addiction severity prediction, and ethical intervention in educational and healthcare environments.

4. OPPORTUNITIES AND LIMITATIONS

   While existing AI frameworks have laid a strong foundation, several limitations remain. Current systems often rely on rigid mathematical models or discrete decision trees that might miss nuances in frequency, urge, and impact. Furthermore, analyz- ing social media datasets typically occurs post-event, which limits the ability to provide real-time, personalized counseling to the user. Standard machine learning models generally output a simple label without providing the contextual reasoning behind the risk level.

   These limitations present a significant opportunity for the integration of Large Language Models (LLMs) into the pre- ventative healthcare domain. By deploying these systems as accessible web applications, interventions can move beyond simple classification to contextual reasoning. Utilizing models like Google Gemini allows for the analysis of structured survey responses (such as CRAFFT and ASSIST) combined with prompt engineering to detect behavioral nuances that strict mathematical models might miss [4], [5]. Acting as an expert system, the LLM evaluates patterns in substance use frequency, urge, and impact to generate personalized reasoning. This technological shift enables the creation of an empathetic AI Chatbot that provides 24/7 counseling, mentor- ship, and motivational guidance through natural, human-like conversations rather than rigid menu options.

5. CONCLUSION

This review presented a comprehensive analysis of AI-based drug abuse detection approaches, including behavioral and lin- guistic analysis, decision-based severity modeling, and neural network-based content detection. Each approach was examined in terms of its methodology, strengths, and limitations with respect to early identification and practical deployment

The review highlights a significant gap in the availabil- ity of integrated, explainable, and ethically deployable drug abuse detection systems. Most existing approaches operate independently and lack alignment with standardized screening tools such as CRAFFT and ASSIST [4], [5]. Addressing these limitations through unified AI-assisted screening frameworks can significantly enhance early detection, continuous monitor- ing, and timely intervention. Future research should focus on institution-centric, privacy-preserving, and explainable AI web applications for effective drug abuse prevention.

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